Method and apparatus for providing a personal value for an individual

ABSTRACT

A method and apparatus for generating a personal value for a user are disclosed. For example, the method collects data associated with the user, wherein the data that is collected comprises social connection data, enhances the data that is collected, receives a request, and generates a response to the request by using a personal value for the user, where the personal value is generated from the data that is enhanced.

The present disclosure relates generally to communication networks and,more particularly, to methods, computer-readable media and devices forgenerating and utilizing one or more personal values for an individual.

BACKGROUND

Personal information for an individual can be readily obtained orvoluntarily provided by the individual. For example, the individual mayprovide information such as a current salary, a financial reportpertaining to the individual's assets, e.g., real estate holdings,financial statements from financial institutions, tax returns, and thelike. Although the above information can be used to provide a rating ofthe individual, e.g., a credit score, the above information does notprovide a true measure of the individual's overall value.

SUMMARY

In one embodiment, the present disclosure discloses a method forgenerating a personal value for a user. For example, the method collectsdata associated with the user, wherein the data that is collectedcomprises social connection data, enhances the data that is collected,receives a request, and generates a response to the request by using apersonal value for the user, where the personal value is generated fromthe data that is enhanced.

BRIEF DESCRIPTION OF THE DRAWINGS

The teachings of the present disclosure can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an exemplary network related to the presentdisclosure;

FIG. 2 illustrates a flowchart of a method for generating and utilizinga personal value for an individual; and

FIG. 3 illustrates a high-level block diagram of a general-purposecomputer suitable for use in performing the functions described herein.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION

The present disclosure broadly discloses methods and computer-readablemedia for generating and utilizing a personal value for an individual.Although the present disclosure may describe embodiments in the contextof particular networks, systems and environments, the present disclosureis not so limited. Namely, the present disclosure can be applied to anytype of computer-enhanced communication network that is capable ofsupporting communications between devices.

In various embodiments, the present disclosure provides a network-basedservice that generates and utilizes one or more personal values for anindividual. For instance, embodiments of the present disclosure may bedeployed in, or provided by, an application server as a service. In oneembodiment, the service accesses and aggregates user informationincluding, inter alia, self-identified data, self-generated data, socialconnection data, societal connection data, financial data, consumerdata, and third party feedback data.

Once the data has been gathered and “enhanced”, an individual'scumulative value or personal value is generated and stored. It should benoted that although the present disclosure discusses the generation of apersonal value for an individual, the present disclosure is not solimited. As will be discussed further below, one or more personal valuescan be generated for each individual. This individual's personal valuecan be perceived as a common stock portfolio that fluctuates based onthe attributes, parameters, or market needs that serve as the basis forcomputing the individual's personal value. In one embodiment, thepersonal value is implemented as a “vector” with multi-dimensionalcomponents that sum into an overall value and direction in n-space.However, it should be noted that the present disclosure may use only oneor more subsets of the overall vector depending on the criteria of aparticular application as discussed further below.

Thus, changes to the attributes or parameters of the individual willcause a corresponding change to the individual's personal value, e.g.,receiving a promotion at work may elevate the individual's personalvalue, whereas losing a job may lower the individual's personal value. Adetailed description as to the generation and utilization of theindividual's personal value will be described below.

In one embodiment, the individual's personal value (including theunderlying parameters or attributes that were used to generate thepersonal value) can be centrally stored, e.g., in a database. Forexample, an application server may obtain the individual's personalvalue from e.g., a web-based application server. It should be noted thatthe present disclosure is not limited by the hardware devices or thelocation of the hardware devices that are used to store the individual'spersonal value, i.e., a web-based application server is only anillustrative example.

In one embodiment, the service may access the individual's personalvalue to provide an event opportunity. For example, a third party vendormay be interested in marketing a product or a service to variousindividuals. However, the product or the service may be deemed to beappropriate only for individuals with personal values greater than apredefined value. For example, a power boat vendor may want to market anew power boat to consumers. However, given that only a small portion ofthe population will be interested or even qualified to purchase a powerboat, the third party vendor may utilize an individual's personal valueto determine whether marketing effort should be directed to a particularindividual. It should be noted that since an individual's privateinformation is very sensitive and quite personal to the individual, theaccess by any third party vendor will be subjected to variouslimitations that will be discussed further below.

In another example, the product or the service may be deemed to beappropriate only for individuals with personal values lower than apredefined value. For example, a discount store (e.g., a discount storewhere all items in the store are priced less than $1.99) may beinterested in opening a new store at a new location and may select alocation where there is a high concentration of individuals where theirpersonal values may indicate that they are more spendthrift.

The above two examples provide a quick view of the utilization of theindividual's personal value based predominately on financial strength ofthe individuals. However, the utilization of the individual's personalvalue of the present disclosure is not so limited. For example, if athird party vendor wants to organize a fund raiser at a location for aparticular cause, e.g., for supporting cancer awareness, for supportinga political candidate, for supporting an animal shelter, for supportinga particular educational institutional, e.g., a college or a university,and so on, then an individual's personal value based predominately onfinancial strength alone may not always provide a proper match. Forexample, organizing a fund raiser for a particular political candidatebased solely on a high personal value of individuals in a particularlocation based predominately on financial strength may still produce apoor result without knowing the political affiliations of thoseindividuals. Similarly, organizing a fund raiser for a particularcollege at a location where there are very few alumni from that collegemay still produce a poor result even though many individuals at thatlocation have high personal values.

Thus, the personal value of an individual of the present disclosure isnot based alone on financial data. In fact, as will be discussed furtherbelow, the personal value is generated in view of one or more ofself-identified data, self-generated data, social connection data,societal connection data, financial data, consumer data, and third partyfeedback data. The present approach provides a more accurate assessmentof the true value of an individual. In fact, in one embodiment, thepersonal value may dynamically change based on the purpose of a requestin which the personal value will be used. For example, the personalvalue for an individual for the purpose of buying a power boat may havea score of “1000”, whereas the personal value for the same individualfor the purpose of supporting a Republican candidate at a political fundraiser may have a score of “200” (e.g., because the individual is aregistered Democrat).

To better understand the present disclosure, FIG. 1 illustrates anexample network 100, e.g., an Internet Protocol (IP) network related tothe present disclosure. An IP network is broadly defined as a networkthat uses Internet Protocol to exchange data packets. Exemplary IPnetworks include networks such as Voice over Internet Protocol (VoIP)networks, IP Multimedia Subsystem (IMS) networks, Service over InternetProtocol (SoIP) networks, and the like.

In one embodiment, the network 100 may comprise a plurality of endpointdevices 102-104 configured to provide data or request to a datacollection server 125 through the core network 110 or Internet (e.g., anIP based network supported by a service provider) via an access network101. Similarly, a plurality of endpoint devices 105-107 are configuredfor communication with data collection server 125 through the corenetwork 110 via an ISP using an access network 108. The network elements(NEs) 109 and 111 may serve as gateway servers or edge routers for thenetwork 110.

The endpoint devices 102-107 may comprise user endpoint devices such aspersonal computers, servers, laptop computers, Personal DigitalAssistants (PDAs), mobile phones, cellular phones, smart phones,computing tablets, email devices, messaging devices, home automationdevices, machine-to-machine instruments and the like. For example, eachof the endpoint devices may comprise a subscriber's mobile endpointdevice or a vendor/third-party's endpoint device (e.g., a point-of-sale(POS) terminal, a third party server, or a computer, or a home securitysystem), as will be described in greater detail below. The accessnetworks 101 and 108 serve as a conduit to establish a connectionbetween the endpoint devices 102-107 and the Network Elements (NEs) 109and 111 of the core network 110 or Internet. The access networks 101 and108 may each comprise a Digital Subscriber Line (DSL) network, abroadband cable access network, a Local Area Network (LAN), a cellularnetwork, a Wi-Fi network, a 3^(rd) party network, and the like. Theaccess networks 101 and 108 may be either directly connected to NEs 109and 111 of the IMS core network 110, or indirectly through anothernetwork.

In one embodiment, the core network 110 may also comprise a DataCollection Server 125, a Data Enrichment Application 112, anunstructured database 115, a security, permissions, and informationaccess control system 121 (broadly an access control system), and anApplication server 127.

In one embodiment, the access control system 121 broadly determineswhether various requests to obtain data (e.g., accessing an individual'spersonal value) will be allowed, e.g., the requestor may have to undergoan authentication process, and the like. Furthermore, access controlsystem 121 may also have the necessary access information to verifyvarious attributes that are associated with an individual, e.g., accountinformation, user names, access codes, and the like. Broadly, accesscontrol system 121 is used to ensure that personal information would notbe easily obtained without proper authentication or approval provided byan individual, either previously defined, e.g., via a user profile, oron a per request basis, e.g., receiving a direct approval from theindividual.

The application server 127 may comprise any server or computer that iswell known in the art, and the database 115 may be any type ofelectronic collection of data that is also well known in the art and maybe generally preferred to be embodied using unstructured data andschema-less design (e.g. MongoDB). In one embodiment, the applicationserver 127 may be configured to generate and utilize a personal valuefor an individual. Accordingly, in one embodiment the database 115 maystore a list of individuals (e.g., registered users of a serviceprovider such as subscribers to a cellular service and the like) andtheir corresponding personal values and enable the arrangement of eventopportunities between the list of individuals and third party vendors,as discussed in greater detail below.

The above IP network is only described to provide an illustrativeenvironment in which data is transmitted, stored, and/or processed innetworks. It should be noted that the communication network 100 may beexpanded by including additional endpoint devices, access networks,network elements, application servers, etc. without altering the scopeof the present disclosure.

FIG. 2 illustrates a flowchart of a method 200 for generating andutilizing a personal value for an individual. In one embodiment, one ormore steps of the method 200 can be performed by one or more of thecomponents of the core network 110 and/or similar elements if they wereto be added to the access networks 101 and 108, which may comprisewireless or wireline access networks. For example, in one embodiment oneor more steps of the method 200 can be implemented by an applicationserver such as 127. In addition, one or more steps of the method 200 maybe implemented by a general purpose computer having a hardwareprocessor, a memory and input/output devices as illustrated below inFIG. 3, where the general purpose computer serves as an illustrativephysical architecture for the application servers or computing systemsas discussed in the present disclosure.

The method begins in step 205 and proceeds to step 210. At step 210, themethod 200 collects information for an individual. For example, one ormore self-identified data, self-generated data, social connection data,societal connection data, financial data, consumer data, and third partyfeedback data are collected for each individual.

In one embodiment, self-identified data comprises user preferences, userperception data, and the like. Broadly, self-identified data comprisesany information that is dictated and specified by the user based solelyat the judgment or personal opinion of the user. For example, the usermay specify that he or she likes a particular type of food, a particularrestaurant, a particular area of town, a particular brand of clothing, aparticular place to vacation, a particular trait in a person that he orshe finds attractive, a particular trait in a person that he or shefinds unattractive, a favorite sport, a favorite professional sportsteam, a favorite college team, a favorite color, a favorite celebrity, afavorite show, and so on. The above list is only illustrative and shouldnot be interpreted as a limitation of the present disclosure. In sum,self-identified data broadly comprises the user's preferences oropinions.

In one embodiment, self-generated data comprises any information that isgenerated or caused by the user that can be factually verified. Forexample, self-generated data may comprise the user's address (e.g., asverified by a utility bill), the user's official job title, the user'semployer (e.g., as verified by a W-2 form), the current physicallocation of the user (e.g., as verified by a GPS signal from a userdevice, e.g., the user's cell phone), the user's health information(e.g., as verified by medical records), the user's daily schedule (e.g.,as verified by the user's electronic calendar), the user's drivingrecords, (e.g., as verified by the department of motor vehicle for aparticular state), the type and make of a vehicle owned and/or driven bythe user, (e.g., as verified by the user's car title and vehicleregistration), the educational degrees obtained by the user (e.g., asverified by a college transcript), the professional associations of theuser (e.g., as verified by membership to a professional organization forengineers, teachers, doctors, nurses, lawyers, etc.), the type of petsowned by the user (e.g., as verified by an animal license), the user'stravel records (e.g., frequent flier miles, frequent reward points at ahotel chain, etc.) and so on. The above list is only illustrative andshould not be interpreted as a limitation of the present disclosure. Insum, self-generated data broadly comprises data that are caused oreffected by the user that can be verified or validated.

In one embodiment, social connection data comprises any information thatrelates to people known to the user or where the people have previouslybeen associated with or interacted with the user. For example, socialconnection data may comprise family members or relatives (e.g.,grandparents, parents, children, grandchildren, spouses, brothers,sisters, uncles, aunts, nieces, nephews, in-laws, and so on), friends,colleagues (e.g., from work, from professional organizations, and thelike), business associates (e.g., vendors, competitors, customers, andthe like), and so on. The above list is only illustrative and should notbe interpreted as a limitation of the present disclosure. In sum, socialconnection data broadly comprises data that contains people who are“associated” with the user, e.g., broadly people who may have interactedwith the user in some manner.

In one embodiment, societal connection data comprises any informationthat relates the user to one or more “societies”. For example, societalconnection data may comprise associating the user to a society based ona user's present or former address or a location (e.g., as a“Southerner” as verified by the user address being in the southernUnited States, a “New Yorker” as verified by the user address being inNew York City), based on a religion (e.g., as a Christian, a Catholic, aProtestant, a Baptist, a Muslim, a Buddhist, a Hindu, and so on), basedon a political affiliation (e.g., an Independent, a Democrat, aRepublican, and so on), based on a professional or trade society (e.g.,a certified surgeon, a certified accountant, a certified engineer, acertified trial attorney, a certified home inspector, and so on), basedon a governmental society or agency (e.g., district attorneys from theJustice Department, engineers from the Environmental Protection Agency,patent examiners from the U.S. Patent and Trademark Office, and thelike), and so on. The above list is only illustrative and should not beinterpreted as a limitation of the present disclosure. In sum, societalconnection data broadly comprises any data that discloses the societiesthat the user is associated with.

In one embodiment, financial data (or business data) comprises anyinformation that relates the user's financial position or financialinfluence or capability. For example, financial data may comprise theuser's real estate holdings, the user's salary, the user's personalfinancial information (e.g., insurance policies, bank account and/orbrokerage account information and associated values in those accounts),the user's credit information (e.g., credit scores, credit cardaccounts, loans, mortgages, student loans, home equity loans, car loans,and the like), and so on. The above list is only illustrative and shouldnot be interpreted as a limitation of the present disclosure. In sum,financial data broadly comprises any data that discloses the financialvalue of the user.

In one embodiment, consumer data comprises any information that relatesto the user's consumption, purchases or spending. For example, consumerdata may comprise the user's shopping habits or tendencies such asstores (e.g., the physical stores that the user will shop at (e.g.,department stores, supermarkets, etc.)), web sites visited by the userto make purchases (e.g., Amazon.com, etc.), types of goods (e.g.,electronic, clothing, shoes, food, furniture, etc.) and servicespurchased by the user, the payment type (e.g., credit cards, debit card,cash, or coupons), the time that the user will shop (e.g., time of day,day of week, month of year, particular holidays, etc.), dollar amountspent (e.g., for each type of transaction, for each type of item, foreach type of service, etc.), tendencies to transact at various levels(e.g., willing to make large purchases, e.g., cars, vacations,furniture, or unwilling to make large purchases), viewing or browsingbehavior (e.g., the amount of time viewing items at a particularwebsite), making donations (e.g., making charitable donations to variouscharities or causes), and so on. The above list is only illustrative andshould not be interpreted as a limitation of the present disclosure. Insum, consumer data broadly comprises any data that discloses broadly thespending behavior of the user.

In one embodiment, third party feedback data comprises any informationthat relates to the feedback directed to the user. For example, thirdparty feedback data may comprise third party feedback related to theuser's professional skill (e.g., the user is a good or bad doctor, theuser is a good or bad lawyer, the user is a good or bad teacher, and thelike), third party feedback related to the user's popularity orreputation (e.g., the user is a popular student based on the number ofuser's connections to other students on a social network website), theuser is a popular celebrity (e.g., an actor, a singer, a performer, anathlete) based on a number of “followers” on a social network website,and the like), third party feedback related to the user's activity onthe web (e.g., the user has a good rating on an auctioning site likeeBAY, the user has a good rating as a good gamer for a particular videogame at a gaming site, etc.), and so on. It should be noted that thirdparty feedback, e.g., of a professional's reputation may also becollected and aggregated from business/service review web services. Theabove list is only illustrative and should not be interpreted as alimitation of the present disclosure. In sum, third party feedback databroadly comprises any feedback data provided by others (not the user)relating to the user's behavior, reputation or skills.

It should be noted that the data collection step 210 of FIG. 2 is not astatic one-time event. In fact, the data collection step 210 is anongoing process, e.g., sources of various data are stored and thecollected data can be updated from these identified sources on a regularbasis, e.g., hourly, daily, weekly, monthly, quarterly, or yearly. Infact, new sources of data can be made known to the present method at anytime, e.g., the user has a new job, the user opened a new credit card,the user relocated to another city, the user visited a new web site, theuser purchased a new car, and so on.

Returning to FIG. 2, once the data is collected in step 210, the method200 proceeds to step 220 where the collected data can be enhanced orenriched, e.g., via the data enrichment application 112. For example, asdata are collected, various meta-data are also created. The date andtime of creation and collection, source, purpose, location, persona,device, sensitivity, and other aspects of the data collection processare included in the metadata and can help characterize the validity orveracity of the collected data. For example, some data may need to beverified, e.g., via an automated process. Specifically, analytics canhelp with automated verification (e.g., statistical veracity) and canalso derive new data from inputs and measurements. Analytics can includecorrelation, corroboration, and trending. For example, a user's addresscan be verified through a utility service subscribed by the user. Inanother example, a user's educational qualification can be verified withthe university attended by the user, and so on. Thus, the collected data(e.g., self-generated data, social connection data, societal connectiondata, financial data, consumer data, and third party feedback data) canbe enhanced in one embodiment, through verification or validation of thecollected data through an independent authoritative source. However, itshould be noted that not all collected data requires verification orvalidation, e.g., self-identified data need not be verified orvalidated.

Furthermore, enhancement of the collected data may involve the creationof additional data, e.g., correlation of various collected data todeduce additional data. For example, if the user is noted to haveattended numerous sports events at a particular sports stadium, then themethod may deduce that the user is a sports fan of a local sports teamthat is affiliated with that particular stadium. In another example, ifthe user has contributed consistently to political candidates of aparticular party, then the method may deduce that the user favors thatparticular political party. Thus, method 200 may not only verify thecollected data in step 220, but may automatically generate variousinferences that can further supplement the data that has been collectedfor a particular user. Thus, method 200 may enhance the collected databy verifying the collected data and/or generating new data throughcorrelations of the collected data.

In one embodiment, the data enhancement step will keep track of thesource data used to derive new data components, and there will be asecurity mechanism that tracks inheritance of sensitive information sothat such data is not revealed or leaked through derivative forms. Forexample, the financial and medical data of the user should not bedisclosed, i.e., a request for individuals with yearly incomes of$200,000 should not result in a list of individuals who have yearlyincomes of $200,000.

However, aggregates and other forms of derived sensitive data may becomenon-sensitive through the enrichment process. For example, a request forindividuals with yearly incomes of $200,000 may result in an automatedresponse that identifies a geographic location where within 50 miles ofthis identified location, there are 10 individuals with yearly incomesof $200,000, or 2% of the households within 50 miles of this identifiedlocation does meet the requested requirement. In this fashion, in oneembodiment sensitive information will not be provided and the dataenhancement step serves as a mechanism to desensitize the collecteddata.

In one embodiment, the system may automate the generation of securityvectors. The notion of a “security vector” is that the system should beable to differentiate different levels of security among its users. Forexample, a personal calendar may be considered off-limits to the generalpublic, but may be accessible to users identified as family members.Specific appointments may be shared with business partners, andfree-busy time may be provided to a broader set of users. Thisfine-grained and pointed security vector approach enables a high levelof trust in the present method, thereby encouraging the willingness ofthe user to share personal data. In addition to the security aspect, thepresent disclosure does provide the ability to auto-accept access tovalue information of an individual based on preferences. In other words,there is a balance between protecting an individual's personalinformation, while also providing sufficient access to the valueinformation of an individual so that the individual may benefit fromsuch access.

In step 230, method 200 generates a personal value for the user, e.g., ascore from 1-100, 1-1000, or any other ranges of numbers. It should benoted that step 230 can be an optional step at this point. In otherwords, the method 200 may not generate a personal value at this point,but only store the collected and enhanced data. In fact, the personalvalue can be generated after step 250 as discussed further below after arequest is received. Thus, in one embodiment, the personal value isdynamically generated, i.e., generated on demand from the stored data.Thus, the generated personal value is not a static indicator of theindividual's personal value. In other words, the personal value maychange based on specific request parameters or target query as discussedbelow.

For example, the above example provided a list of broad data types thatcan be considered, e.g., 1) self-identified data, 2) self-generateddata, 3) social connection data, 4) societal connection data, 5)financial data, 6) consumer data, and 7) third party feedback data.These data types can be viewed as attributes that contribute to thepersonal value of the user. For example, a default value (or a range ofvalues) can be assigned to each attribute. To illustrate, one approachmay have a personal value (PV) that ranges from a score of 1-1000,where:

PV = self-identified  data  value + self-generated  data  value + social  connection  data  value + societal  connection  data  value + (z^(*)  financial  data  value) + consumer  data  value + third  party  feedback  data  value.

In this illustrative example, each of the self-identified data value,the self-generated data value, the social connection data value, thesocietal connection data value, the financial data value, the consumerdata value, and the third party feedback data value, may have a maximumvalue of 100, thereby resulting at a maximum score of 700. In thisillustrative example, the financial data value has a weight ormultiplier “z” (e.g., set at a value of 4), that will weigh thefinancial data value four times more than any other attributes resultingin a maximum score of 1000 (i.e., 100 points for each of the attributes,with the exception that 400 points are assigned to the financial dataattribute). It should be noted that a weight or multiplier can beapplied to any of the above attributes and the above example is only anillustration.

It should be noted that each of the attributes may have sub-attributes.To illustrate, the financial data attribute may have the followingsub-attributes:

-   1) home ownership: (10 points for owning home for more than 20    years, 5 points for owning home for more than 10 years, 0 point for    not owning a home).-   2) equity in home: (10 points for having more than $200,000 in home    equity, 5 points for having more than $100,000 in home equity, 0    point for having less than $5,000 in home equity).-   3) salary range: (10 points for having a salary greater than    $200,000, 5 points for having a salary greater than $100,000, 0    point for having a salary less than $30,000).-   4) stock ownership: (10 points for having more than $200,000 in    stock, 5 points for having more than $100,000 in stock, 0 point for    having less than $5,000 in stock).-   5) debt or liability: (10 points for having no debt or liability, 5    points for having less than $200,000 in debt or liability, 0 point    for having more than $200,000 in debt, −10 points for having more    than $500,000 in debt).-   6) cash in banks: (10 points for having more than $200,000 in banks,    5 points for having more than $5,000 in banks, 0 point for having    less than $5,000 in banks).-   7) cash in retirement account: (10 points for having more than    $200,000 in retirement account, 5 points for having more than $5,000    in retirement account, 0 point for having less than $5,000 in    retirement account).-   8) life insurance policy: (10 points for having more than $500,000    in life insurance policy, 5 points for having more than $50,000 in    life insurance policy, 0 point for having no life insurance policy).-   9) equity in a personal business: (10 points for having more than    $200,000 in equity in a personal business, 5 points for having more    than $5,000 in equity in a personal business, 0 point for having    less than $5,000 in equity in a personal business).-   10) expected inheritance: (10 points for having more than $200,000    in expected inheritance, 5 points for having more than $5,000 in    expected inheritance, 0 point for having less than $5,000 in    expected inheritance).

To illustrate another example, the social connection data attribute mayhave the following sub-attributes:

-   1) Number of individuals known by user: (20 points for knowing at    least 500 individuals, 15 points for knowing at least 300    individuals, 10 points for knowing at least 150 individuals, 5    points for knowing at least 25 individuals, 0 point for knowing less    than 10 individuals).-   2) Average personal score of individuals known by user: (20 points    for average personal score of individuals known by user is greater    than 750, 15 points for average personal score of individuals known    by user is greater than 500, 10 points for average personal score of    individuals known by user is greater than 150, 0 point for average    personal score of individuals known by user is less than 150).-   3) Average salary of individuals known by user: (20 points for    average salary of individuals known by user is greater than    $100,000, 15 points for average salary of individuals known by user    is greater than $75,000, 10 points for average salary of individuals    known by user is greater than $30,000, 0 point for average salary of    individuals known by user is less than $30,000).-   4) Average number of years of friendship of individuals known by    user: (20 points for average number of years of friendship of    individuals known by user is greater than 20 years, 15 points for    average number of years of friendship of individuals known by user    is greater than 15 years, 10 points for average number of years of    friendship of individuals known by user is greater than 3 years, 0    point for average number of years of friendship of individuals known    by user is less than 3 years).-   5) Number of individuals known by user who consider the user to be a    close friend or relative: (20 points for knowing at least 50 of such    individuals, 15 points for knowing at least 25 of such individuals,    10 points for knowing at least 5 of such individuals, 0 point for    knowing less than 5 of such individuals).

It should be noted that the above set of attributes and the associatedpoint assignments are only illustrative for the purpose of explainingthe calculation of the personal value. As such, the above examplesshould not be perceived as a limitation of the present disclosure.

Once the user's personal value is calculated, the personal value isstored in a database. As discussed above, this personal value can beperiodically updated on a predefined schedule and/or when new data iscollected for the user. It should be noted that although steps 210-230are described for a single individual, the present disclosure is not solimited. Namely, steps 210-230 are repeated for a plurality ofindividuals, e.g., the individuals can be subscribers of atelecommunication service, the users can be cellular phone servicesubscribers, the users can be customers of particular web sites, theusers can be users of a social network site, and so on.

In one embodiment, the stored personal value as calculated and storedcan be changed dynamically based upon receiving a request as furtherdiscussed below. To illustrate, if John Doe has a personal value of 700and Jane Doe has a personal value of 750, and a request is received bythe present method to identify those individuals with personal values of700 or greater for a political function for a Republican candidate, thenthe present method may not automatically deem both John Doe and Jane Doeto be appropriate matches for the request. For example, if John Doe is aregistered Democrat, then this one piece of data may be heavily weightedin the context of the purpose of the request. In other words, knowingthe purpose of a particular request may dynamically alter the previouslycalculated personal value. For example, a negative weight may beassigned to this one attribute such that John Doe's personal value forthe purpose of this one particular request may actually fallsignificantly to 350, thereby falling well short of the requestedpersonal value of 700 or greater as defined by the request. This dynamicapproach in recalculating or adjusting the personal value in view of thenature of the request will provide greater accuracy in terms of theresponses that will be provided to the various different requests. Thus,in one embodiment, the personal value will not only dynamically changebased on the collected data, but it may also change based on the intentof a particular request.

In addition, a personal value attribute may actually change due toexternal events. For example, as more and more COBOL programmers retire,the personal value of a working COBOL programmer may dynamicallyincrease due to scarcity of the resource. In other words, the value ofeach attribute or dimension that is used to compute the personal valuemay dynamically change based on external events.

Returning to FIG. 2, once the personal values have been calculated for aplurality of individuals, the stored personal values can be used forresponding to targeted requests or inquires. For example, a third partyvendor may want to market a particular product or service only to aparticular group of individuals based on the individuals' personalvalue. For example, a country club may want to offer a free round ofgolf to potential customers who may eventually become members of thecountry club. However, the owner of the country club may not have thenecessary resources to properly identify which individuals should betargeted for this particular promotion, especially when the promotionhas a very high value. Although an individual who has a high income maybe a potential target, using such isolated piece of financialinformation does not readily produce the desired result. Similarly,using a list of friends of current members of the country club by itselfmay also produce spotty results given that there is no informationpertaining to the golf interests and financial capabilities of thesefriends of the current members.

To illustrate, if the personal value as discussed in the presentdisclosure contains one or more attributes that track sports interestsof the users and their memberships to various clubs, e.g., countryclubs, then individuals who have a high personal value who are alsoconnected to other individuals who are current members of thisparticular country club, can be identified for receiving this veryvaluable promotion. The reason is that these particular individuals maybe enticed to become members once they had an opportunity to play around of free golf at this country club given that they already knowpeople who are members.

Furthermore, in one embodiment, the request may actually originate froma user who has a calculated personal value. For example, the user mayinquire as to a local restaurant that has been frequented by others withthe same personal value as the requesting user.

Thus, in step 240, the method 200 receives a request or inquiry that canbe serviced by using one or more of the plurality of stored personalvalues of various individuals. The request can be generic, e.g., howmany individuals within a particular location have a personal value of700 or greater. The request can be very specific, e.g., how manyindividuals within a particular location have a personal value of 700 orgreater, who previously attended a particular university, and arecurrently employed as engineers.

In step 250, method 200 determines whether the access of the user'spersonal value is permitted. For example, the user may have specifiedvia a user profile as to what situations or scenarios the user'spersonal value is to be accessed and used in response to a request orinquiry. For example, the user may specify that his or her personalvalue can be accessed by any third party as long as no specific personalinformation is provided in the response, e.g., the personal value isonly used in an aggregate response, e.g., the user is one of 1000 usersthat meet the requirement of an inquiry without specifically identifyingthe user. Alternatively, the user may specify that his or her personalvalue cannot be accessed by any third party, but can only be used toassist a request issued by the user himself. In yet another alternative,the user may specify that his or her personal value can only be accessedby a particular third party, e.g., a university's placement office (or ahead hunter firm) that is attempting to match a potential employer withan individual, e.g., a student, a particular professional, and so on.

In one embodiment, method 200 may actively solicit permission from theuser (broadly user input). For example, if a request is received thatwill involve a particular user based on the user's personal value, themethod may contact the user to ask if the user is willing to allow thesystem to divulge information pertaining to the user. For example, themethod may detect that the user has been matched to a promotion offeredby a third party based on the user's personal value. However, before themethod is allowed to provide any of the user's information, the usermust first be willing to receive the promotion. This safeguard allowsthe user to determine whether a particular promotion is worthy ofproviding the user's personal information to the third party vendor.

In one embodiment, the user's participation in allowing his or herpersonal value to be accessed should be incentivized. For example, arequest for personal value information may be accompanied by anincentive to the individual, i.e., a boost in one aspect of the user'spersonal value or exclusivity to an offer (e.g., a new product or a newservice) and the like.

If the access is not permitted, the method 200 proceeds to step 265. Ifthe access is permitted, then the method 200 proceeds to step 260 wherea response is provided to the request based on the personal value of atleast one user. It should be noted that the response may provide aportion of the personal information related to the at least one user tothe requester, e.g., an email address of the user, a cell phone numberof the user, a mailing address of the user, a PO box address of theuser, and the like. Alternatively, the user's personal value is onlyused in an aggregate response where no personal information of any ofthe users is provided. In yet another embodiment, the response maysimply be a numerical value, e.g., 99 users matched the requester'sinquiry. In turn, the requester may simply provide the promotionmaterial to the present method, e.g., to the core network serviceprovider, such that the core network service provider will distributethe promotion to the 99 identified users. In this fashion, the requesterhas a level of confidence that the promotion is provided in a verytargeted manner and the user's personal information is safeguarded andthe user is the recipient of a valuable promotion. In one embodiment,the core network service provider may implement method 200 as a networkservice, e.g., where the user is accessed a charge for the serviceand/or the requester is accessed a charge for the service of reaching avery targeted audience. Method 200 ends in step 265.

It should be noted that although not specifically specified, one or moresteps of the method 200 may include a storing, displaying and/oroutputting step as required for a particular application. In otherwords, any data, records, fields, and/or intermediate results discussedin each of the respective methods can be stored, displayed and/oroutputted to another device as required for a particular application.Furthermore, steps or blocks in FIG. 2 that recite a determiningoperation or involve a decision do not necessarily require that bothbranches of the determining operation be practiced. In other words, oneof the branches of the determining operation can be deemed as anoptional step.

FIG. 3 depicts a high-level block diagram of a general-purpose computersuitable for use in performing the functions described herein. Asdepicted in FIG. 3, the system 300 comprises a hardware processorelement 302 (e.g., a CPU), a memory 304, e.g., random access memory(RAM) and/or read only memory (ROM), a module 305 for generating andutilizing a personal value for an individual as described herein, andvarious input/output devices 306 (e.g., storage devices, including butnot limited to, a tape drive, a floppy drive, a hard disk drive or acompact disk drive, a receiver, a transmitter, a speaker, a display, aspeech synthesizer, an output port, and a user input device (such as akeyboard, a keypad, a mouse, and the like)). In one embodiment, module305 may comprise computer/processor executable code containing aplurality of instructions for performing steps of the exemplary method200.

Accordingly, it should be noted that the present disclosure can beimplemented in software and/or in a combination of software andhardware, e.g., using application specific integrated circuits (ASIC), ageneral purpose computer or any other hardware equivalents, e.g.,computer readable instructions pertaining to the method(s) discussedabove can be used to configure a hardware processor to perform the stepsof the above disclosed methods. For example, in one embodiment, themodule or process 305 can be loaded into memory 304 and executed byprocessor 302 to implement the functions as discussed above inconnection with any one or more of the exemplary method 200. As such,the present module or process 305 (including associated data structures)of the present disclosure can be stored on a non-transitory (tangible orphysical) computer readable medium, e.g., RAM memory, magnetic oroptical drive or diskette and the like.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

1. A method for generating a personal value for a user, comprising:collecting data associated with the user, wherein the data that iscollected comprises social connection data; enhancing the data that iscollected; receiving a request; and generating a response to the requestby using a personal value for the user, where the personal value isgenerated from the data that is enhanced.
 2. The method of claim 1,wherein the data that is collected further comprises self-identifieddata.
 3. The method of claim 1, wherein the data that is collectedfurther comprises self-generated data.
 4. The method of claim 1, whereinthe data that is collected further comprises societal connection data.5. The method of claim 1, wherein the data that is collected furthercomprises financial data.
 6. The method of claim 1, wherein the datathat is collected further comprises consumer data.
 7. The method ofclaim 1, wherein the data that is collected further comprises thirdparty feedback data.
 8. The method of claim 1, wherein the enhancing thedata that is collected comprises verifying the data with an independentsource.
 9. The method of claim 1, wherein the enhancing the data that iscollected comprises correlating the data to generate new data.
 10. Themethod of claim 1, wherein the enhancing the data that is collectedcomprises desensitizing the data.
 11. The method of claim 1, wherein therequest is from a third party vendor and the personal value is generatedin response to at least one parameter of the request.
 12. The method ofclaim 11, wherein the response is generated in accordance with a userprofile that allowed the personal value to be used.
 13. The method ofclaim 11, wherein the response is generated in accordance with a userinput that allowed the personal value to be used.
 14. The method ofclaim 1, wherein the request is from the user.
 15. A non-transitorycomputer-readable medium having stored thereon a plurality ofinstructions, the plurality of instructions including instructionswhich, when executed by a processor, cause the processor to perform amethod for generating a personal value for a user, comprising:collecting data associated with the user, wherein the data that iscollected comprises social connection data; enhancing the data that iscollected; receiving a request; and generating a response to the requestby using a personal value for the user, where the personal value isgenerated from the data that is enhanced.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the data that is collectedfurther comprises self-identified data, self-generated data, societalconnection data, financial data, consumer data, and third party feedbackdata.
 17. The non-transitory computer-readable medium of claim 15,wherein the enhancing the data that is collected comprises verifying thedata with an independent source.
 18. The non-transitorycomputer-readable medium of claim 15, wherein the enhancing the datathat is collected comprises correlating the data to generate new data.19. The non-transitory computer-readable medium of claim 15, wherein theenhancing the data that is collected comprises desensitizing the data.20. An apparatus for generating a personal value for a user, comprising:a processor; and a computer-readable medium in communication with theprocessor, wherein the computer-readable medium having stored thereon aplurality of instructions, the plurality of instructions includinginstructions which, when executed by the processor, cause the processorto perform a method, comprising: collecting data associated with theuser, wherein the data that is collected comprises social connectiondata; enhancing the data that is collected; receiving a request; andgenerating a response to the request by using a personal value for theuser, where the personal value is generated from the data that isenhanced.